Method for assessing the size of residual metal in 10kV XLPE cables
By detecting microwave signals in the 22GHz~30GHz frequency band of 10kV XLPE cables, extracting the characteristic frequency and amplitude of microwave reflection, and calculating the amplitude factor and metal residue factor, the problem of accuracy in detecting internal metal residues in cables was solved, and efficient and reliable defect assessment was achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- FOSHAN POWER SUPPLY BUREAU GUANGDONG POWER GRID
- Filing Date
- 2025-06-25
- Publication Date
- 2026-05-15
AI Technical Summary
Existing technologies cannot efficiently and accurately detect residual metal defects inside 10kV XLPE cables, which leads to a decline in cable insulation performance and affects the reliability of long-term operation.
Non-contact testing of 10kV XLPE cables was performed using microwave signals in the 22GHz~30GHz frequency band. Multiple characteristic frequencies and their amplitudes were extracted through microwave reflection curves, and amplitude factors and metal residue factors were calculated to assess the size of metal residue defects in the cables.
It improves the detection sensitivity of minute metallic foreign objects without damaging the cable structure, enhances the accuracy and reliability of detection, can quantitatively characterize residual metal defects, and improves the interpretability and engineering applicability of detection results.
Smart Images
Figure CN120594557B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of 10kV XLPE cable testing and evaluation technology, and in particular to a method for evaluating the size of residual metal in 10kV XLPE cables. Background Technology
[0002] Cross-linked polyethylene (XLPE) insulated cables are widely used in various fields such as power, infrastructure, and transportation due to their excellent electrical properties, heat resistance, and chemical stability. In recent years, with the continuous development of related industries, the market demand for XLPE cables has maintained steady growth, and it is expected to continue to show a steady expansion trend in the future.
[0003] With the continuous expansion of applications, the requirements for cable quality are also increasing. However, in actual production, XLPE cables may have foreign metal residues inside due to impure raw materials, improper process control, or equipment and operational problems. These metal residues can become a source of electric field distortion, inducing partial discharge, short circuits, and leakage, ultimately causing a decline in cable insulation performance and affecting its long-term operational reliability. Therefore, how to efficiently and accurately detect potential metal residue defects inside cables has become a crucial technical issue for ensuring cable quality and the safe operation of power systems. Summary of the Invention
[0004] The purpose of this application is to at least solve one of the aforementioned technical deficiencies, particularly the technical deficiency in the prior art regarding how to efficiently and accurately detect potential metal residue defects inside cables.
[0005] Firstly, this application provides a method for evaluating the size of residual metal in a 10kV XLPE cable, the method comprising:
[0006] For the 10kV XLPE cable to be evaluated, a microwave signal with a radial frequency band of 22GHz~30GHz was injected, and multiple microwave reflection characteristic frequencies and their corresponding amplitudes were extracted based on the obtained microwave reflection curves.
[0007] Calculate the amplitude factor corresponding to each microwave reflection characteristic frequency based on each microwave reflection characteristic frequency and its corresponding amplitude.
[0008] The metal residue factor of the cable to be evaluated is calculated based on the amplitude and amplitude factor corresponding to each microwave reflection characteristic frequency.
[0009] The size of residual metal defects in the cable under evaluation is assessed based on the residual metal factor.
[0010] In one embodiment, the step of extracting multiple microwave reflection characteristic frequencies and their corresponding amplitudes based on the acquired microwave reflection curve includes:
[0011] The frequency bands of microwave signals are divided into low frequency band, mid frequency band and high frequency band. The low frequency band is 22GHz~24GHz, the mid frequency band is 24GHz~28GHz, and the high frequency band is 28GHz~30GHz.
[0012] In the microwave reflection curve, the frequencies and amplitudes corresponding to the maximum peak values in the low-frequency, mid-frequency, and high-frequency bands are extracted respectively to obtain multiple microwave reflection characteristic frequencies and their amplitudes.
[0013] In one embodiment, the step of calculating the amplitude factor corresponding to each microwave reflection characteristic frequency based on each microwave reflection characteristic frequency and its corresponding amplitude includes:
[0014] Calculate the amplitude factor corresponding to each microwave reflection characteristic frequency using the following expression:
[0015]
[0016] in, Indicates the first The amplitude factor corresponding to each microwave reflection characteristic frequency. Indicates the first The amplitude corresponding to each microwave reflection characteristic frequency. Indicates the first One microwave reflection characteristic frequency, Indicates the first Frequency weights of microwave reflection characteristic frequencies.
[0017] In one embodiment, the expression for the frequency weight of each microwave reflection characteristic frequency is:
[0018]
[0019] in, Indicates the first Frequency weights of microwave reflection characteristic frequencies Indicates the first One microwave reflection characteristic frequency.
[0020] In one embodiment, the step of calculating the metal residue factor of the cable to be evaluated based on the amplitude and amplitude factor corresponding to each microwave reflection characteristic frequency includes:
[0021] The metal residue factor is calculated using the following expression:
[0022]
[0023] in, Indicates metal residue factor, Indicates the first The amplitude factor corresponding to each microwave reflection characteristic frequency. Indicates the first The amplitude corresponding to each microwave reflection characteristic frequency. This indicates the outer radius of the cable insulation layer of the cable to be evaluated. This indicates the conductor radius of the cable to be evaluated. Indicates the nonlinear factor. This represents the relative permittivity of the insulating material.
[0024] In one embodiment, the step of assessing the size of residual metal defects in the cable to be evaluated based on a residual metal factor includes:
[0025] If the metal residue factor is greater than zero and not greater than the first preset threshold, then the metal residue defect of the cable to be evaluated is small.
[0026] If the metal residue factor is greater than the first preset threshold and not greater than the second preset threshold, then the metal residue defect of the cable to be evaluated is moderate.
[0027] If the metal residue factor is greater than the second preset threshold but not greater than 1, then the cable to be evaluated has a large metal residue defect.
[0028] In one embodiment, the first preset threshold is 0.359807 and the second preset threshold is 0.371104.
[0029] Secondly, this application provides a device for evaluating the size of residual metal in a 10kV XLPE cable, the device comprising:
[0030] The microwave reflection curve acquisition module is used to radially inject microwave signals in the frequency band of 22GHz~30GHz into the 10kV XLPE cable to be evaluated, and extract multiple microwave reflection characteristic frequencies and their corresponding amplitudes based on the acquired microwave reflection curve.
[0031] The amplitude factor calculation module is used to calculate the amplitude factor corresponding to each microwave reflection characteristic frequency based on each microwave reflection characteristic frequency and its corresponding amplitude.
[0032] The metal residue factor calculation module is used to calculate the metal residue factor of the cable to be evaluated based on the amplitude and amplitude factor corresponding to each microwave reflection characteristic frequency.
[0033] The metal residual defect size assessment module is used to assess the size of metal residual defects in the cable to be evaluated based on the metal residual factor.
[0034] Thirdly, this application provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of any of the 10kV XLPE cable metal residue size assessment methods described in the above embodiments.
[0035] Fourthly, this application provides a computer device, including: one or more processors, and a memory;
[0036] The memory stores computer-readable instructions, which, when executed by one or more processors, perform the steps of any of the 10kV XLPE cable metal residue size assessment methods described in the above embodiments.
[0037] As can be seen from the above technical solutions, the embodiments of this application have the following advantages:
[0038] The method for assessing the size of residual metal in 10kV XLPE cables provided in this application utilizes high-frequency microwave signals for non-contact detection of 10kV XLPE cables, enabling effective assessment of internal metal residues without damaging the cable structure. Specifically, injecting microwave signals in the 22GHz~30GHz frequency band into the cable improves the detection sensitivity for minute metal foreign objects, enhances the ability to perceive changes in high-frequency response, and thus increases the defect detection rate. By acquiring microwave reflection curves and extracting multiple characteristic frequencies and their amplitudes, the response characteristics of the cable's internal structure to electromagnetic waves can be more comprehensively characterized, enhancing the detail and stability of the detection. Furthermore, the calculation of amplitude factors helps to standardize the amplitude response differences at different characteristic frequencies, thereby improving the distinction between metal foreign object signals and background noise. Moreover, by calculating a metal residue factor by combining amplitude and amplitude factors, multiple local reflection characteristics can be integrated into a unified index, enhancing the ability to characterize the overall impact of metal residues. Finally, assessing defect size based on the metal residue factor helps to achieve quantitative characterization of defects, improving the accuracy, reliability, and engineering applicability of cable inspection, and possessing significant technical value and application prospects. Attached Figure Description
[0039] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0040] Figure 1 A schematic flowchart illustrating the method for evaluating the size of residual metal in a 10kV XLPE cable provided in this application embodiment;
[0041] Figure 2 A schematic diagram of the structure of the 10kV XLPE cable metal residue size assessment device provided in the embodiments of this application;
[0042] Figure 3 This is a schematic diagram of the internal structure of a computer device provided in an embodiment of this application. Detailed Implementation
[0043] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0044] This application provides a method for evaluating the size of residual metal in 10kV XLPE cables. The following embodiments illustrate this method using a computer device as an example. It is understood that the computer device can be any device with data processing capabilities, including but not limited to a single server, server cluster, personal laptop, desktop computer, etc. Figure 1 As shown, the method may include the following steps:
[0045] S101: For the 10kV XLPE cable to be evaluated, a microwave signal with a radial frequency band of 22GHz~30GHz is injected, and multiple microwave reflection characteristic frequencies and their corresponding amplitudes are extracted based on the obtained microwave reflection curve.
[0046] The cable to be evaluated refers to a 10kV cross-linked polyethylene (XLPE) insulated power cable that has not yet undergone metal residue defect detection. This cable, as the evaluation object, may contain metal foreign objects introduced due to manufacturing defects. Radial injection refers to the introduction of microwave signals into the cable medium from the radial direction, i.e., perpendicular to the cable's longitudinal axis, to penetrate the insulation layer and interact with the internal structure through reflection and scattering. Microwave signals in the 22GHz~30GHz frequency band refer to continuous or pulsed electromagnetic wave signals within this frequency range with high penetration capability and resolution, used to excite the cable structure to generate an electromagnetic response that can be used for feature extraction. The microwave reflection curve is a curve showing the reflection coefficient as a function of frequency, representing the collected cable response signal in the frequency domain, reflecting the cable's reflection characteristics to microwaves of different frequencies. The microwave reflection characteristic frequency refers to the frequency corresponding to a local peak or significant fluctuation on the reflection curve; these frequencies typically exhibit abnormal shifts or enhancements under the influence of metal foreign objects. Amplitude refers to the magnitude of the microwave reflection signal at each characteristic frequency point, reflecting the reflection intensity at that frequency.
[0047] Specifically, the computer equipment works in conjunction with a microwave signal transmitter to excite the 10kV XLPE cable under evaluation with a test signal. Specifically, the computer equipment controls the signal source module to emit a continuous or stepped-scan microwave signal with a frequency range of 22GHz to 30GHz. Through a connected microwave guiding structure, such as a coaxial cable and waveguide adapter, the microwave energy is radially injected into the cable surface, allowing it to penetrate the cable insulation layer in a direction perpendicular to the cable axis and interact with the internal medium or any possible metallic foreign objects, generating a reflected signal.
[0048] Secondly, the computer equipment collects the reflected signals returning from inside the cable using a vector network analyzer or a reflected wave receiving unit, and converts them into digital signals for processing. The computer equipment performs frequency domain transformation on the collected raw reflected signals and generates a microwave reflection curve showing the variation of the reflection coefficient with frequency. This curve is used to present the response differences of the cable structure under different frequency excitations, especially when there are metal residues inside the cable, the reflection characteristics at certain frequency points will be enhanced or distorted.
[0049] Then, the computer equipment uses a pre-defined feature extraction algorithm to identify feature points on the reflection curve. This algorithm may include methods such as first-order derivative analysis, peak detection, local extremum judgment, or adaptive threshold extraction to locate feature frequency points on the curve with significant changes in reflection intensity. For each identified feature frequency point, the computer further extracts the corresponding reflected signal amplitude and records the frequency-amplitude pair. To ensure data quality, a noise filtering module can be configured to remove spurious peaks caused by system background noise or edge effects.
[0050] It is understandable that emitting 22GHz~30GHz microwave signals via radial injection not only avoids damage to the cable structure, but also, due to the strong penetrating power and high sensitivity to metallic media of this frequency band, allows for the acquisition of reflection characteristics without contacting the cable core, thereby achieving effective excitation and identification of metallic foreign objects. Combining the frequency domain reflection curve with characteristic frequency and amplitude extraction, the frequency response information of the signal can be transformed into structural defect characterization information, improving the ability to detect local anomalies.
[0051] S102: Calculate the amplitude factor corresponding to each microwave reflection characteristic frequency based on each microwave reflection characteristic frequency and its corresponding amplitude.
[0052] Among them, the amplitude factor refers to the reflection amplitude parameter after normalization or standardization in a certain way. It is used to enhance the comparability of signal amplitudes at different characteristic frequencies, reduce systematic deviations caused by background conditions, equipment sensitivity, or changes in reflection path, and thus more accurately reflect the actual impact of possible metal residues inside the cable on microwave reflection.
[0053] Specifically, the computer equipment can standardize the amplitude corresponding to each frequency according to a preset amplitude factor calculation model. Specific methods may include, but are not limited to, using the maximum amplitude normalization method, dividing all amplitudes by the maximum reflection amplitude in the current frequency band, thereby mapping the amplitude range to the [0,1] interval; or using the baseline ratio method, comparing each amplitude with the theoretical defect-free response amplitude at the corresponding frequency to form an offset coefficient; or using the noise threshold filtering weighting method, subtracting the background noise baseline from the amplitude and then correcting it according to the noise weight.
[0054] Next, each calculated amplitude factor is bound to its corresponding microwave reflection characteristic frequency, forming a frequency-amplitude factor pair, which is then stored as structured data in the subsequent processing module. The computer equipment can further judge the reasonableness of the amplitude factor results, such as whether they exceed the normal response range or whether there are abnormal spikes, to ensure data quality and avoid subsequent judgment errors caused by false detections.
[0055] It is understandable that by calculating the amplitude factor corresponding to each microwave reflection characteristic frequency, the original reflection amplitude data can be transformed into a standardized quantitative indicator reflecting the degree of defect, thereby effectively improving the comparability of data at different frequencies, reducing the impact of equipment errors and background interference, and enhancing the sensitivity to the response to small metal residues.
[0056] S103: Calculate the metal residual factor of the cable to be evaluated based on the amplitude and amplitude factor corresponding to each microwave reflection characteristic frequency.
[0057] Among them, the metal residue factor is an index calculated based on the amplitude and amplitude factor of a set of reflection characteristic frequencies. It is used to quantify the degree of metal foreign matter residue in the cable to be evaluated. The larger the factor, the more serious the interference of metal foreign matter on the electric field distribution of the cable dielectric, thus affecting its insulation performance.
[0058] Specifically, the computer equipment acquires the amplitude and amplitude factor corresponding to multiple microwave reflection characteristic frequencies, and inputs this set of data into the data processing module in a structured form. Each frequency point forms a complete data item, including the frequency value, the corresponding amplitude, and the normalized amplitude factor, which constitutes the basic unit for subsequent calculations.
[0059] Secondly, the computer equipment executes the comprehensive calculation logic for the metal residue factor. In one implementation, the system uses a weighted summation model to fuse the amplitude and amplitude factor at all frequency points, for example: ,in, Indicates the first The amplitude of each characteristic frequency point This represents the magnitude factor at that point. This represents an optional coefficient used to enhance the weight of a specific frequency point. Different weighting strategies can be configured based on different cable types or detection scenarios, such as assigning higher weights to frequency bands known to be susceptible to metal interference.
[0060] It is understandable that by combining amplitude and amplitude factor for weighted fusion calculation, the microwave response intensity and characteristic sensitivity of the cable at different frequency bands can be comprehensively reflected, improving the overall robustness and accuracy of metal residue assessment. This calculation method can effectively weaken interference factors caused by equipment deviation, background noise, or environmental changes while preserving key reflection characteristics, thereby improving the detection sensitivity to weak metal foreign object responses.
[0061] S104: Based on the metal residue factor, assess the size of the metal residue defects in the cable to be evaluated.
[0062] Among them, the size of metal residual defects refers to the degree of defects determined based on the numerical results of the metal residual factor. It is used to represent the scale of metal foreign objects in the cable, and its size is usually positively correlated with the degree of risk of cable insulation performance degradation.
[0063] Specifically, the computer equipment receives or retrieves metal residue factor data and uses this value as an input parameter for the defect assessment module. This module has multiple preset assessment rules or classification models, and can achieve automatic judgment using methods such as threshold segmentation, fuzzy logic judgment, or machine learning models.
[0064] In one implementation, the computer device compares the value of the metal residue factor with multiple preset thresholds. These thresholds are established based on extensive measured data and cable failure statistics to distinguish different levels of metal residue defects. Furthermore, to improve the adaptability of the judgment, the computer device can dynamically adjust the parameters of the evaluation model or train a classifier model, such as a support vector machine, decision tree, or neural network, based on different cable types, operating environments, or historical fault data, to more accurately match the evaluation needs of specific scenarios. Adaptive evaluation functions for different operating conditions can be achieved by integrating multiple models or policy rules.
[0065] It is understandable that by classifying and evaluating the size of defects based on metal residue factors, abstract microwave reflection characteristics can be transformed into intuitive and quantifiable judgment results, effectively improving the interpretability and engineering applicability of the test results. This facilitates accurate decision-making by on-site technicians or remote monitoring systems regarding cable status and further optimizes the allocation of operation and maintenance resources.
[0066] In the above embodiments, non-contact inspection of 10kV XLPE cables using high-frequency microwave signals enables effective assessment of internal metal residues without damaging the cable structure. Specifically, injecting microwave signals in the 22GHz~30GHz band into the cable improves the detection sensitivity for minute metal foreign objects, enhances the ability to perceive high-frequency response changes, and thus increases the defect detection rate. By acquiring microwave reflection curves and extracting multiple characteristic frequencies and their amplitudes, the response characteristics of the cable's internal structure to electromagnetic waves can be more comprehensively characterized, enhancing the detail and stability of the inspection. Furthermore, the calculation of amplitude factors helps to standardize the amplitude response differences at different characteristic frequencies, thereby improving the distinction between metal foreign object signals and background noise. Moreover, by calculating the metal residue factor by combining amplitude and amplitude factors, multiple local reflection features can be integrated into a unified index, enhancing the ability to characterize the overall impact of metal residues. Finally, assessing defect size based on the metal residue factor helps to achieve quantitative characterization of defects, improving the accuracy, reliability, and engineering applicability of cable inspection, demonstrating significant technical value and application prospects.
[0067] In one embodiment, the step of extracting multiple microwave reflection characteristic frequencies and their corresponding amplitudes based on the obtained microwave reflection curve includes:
[0068] The frequency bands of microwave signals are divided into low frequency band, mid frequency band and high frequency band. The low frequency band is 22GHz~24GHz, the mid frequency band is 24GHz~28GHz, and the high frequency band is 28GHz~30GHz.
[0069] In the microwave reflection curve, the frequencies and amplitudes corresponding to the maximum peak values in the low-frequency, mid-frequency, and high-frequency bands are extracted respectively to obtain multiple microwave reflection characteristic frequencies and their amplitudes.
[0070] Specifically, the computer receives complete microwave reflection curve data from the signal processing module, covering a frequency range of 22 GHz to 30 GHz. The computer then divides this frequency range into three sub-bands according to preset rules: a low-frequency band (22 GHz to 24 GHz), a mid-frequency band (24 GHz to 28 GHz), and a high-frequency band (28 GHz to 30 GHz). This division can be achieved by setting frequency boundary indices to segment the original reflection data sequence, enabling rapid data location within the frequency band.
[0071] Secondly, for each sub-band, the computer equipment sequentially performs a peak search operation. Specifically, a local extremum detection algorithm is used to scan the reflection amplitude data within the band, identify all local peak points, and determine the peak with the largest amplitude. To ensure the authenticity and stability of the peak, a noise threshold filtering strategy can be used to eliminate spurious peaks or edge effects, and a sliding window width is set to avoid overly dense local peak responses. Next, the frequency value and reflection amplitude corresponding to the largest peak are recorded, forming a frequency-amplitude pair, representing the most significant microwave reflection characteristics within the band.
[0072] It is understandable that dividing the entire microwave frequency band into low-frequency, mid-frequency, and high-frequency bands can effectively improve the local resolution of reflection curve analysis, making feature extraction within each sub-band more targeted and avoiding feature weakening or interference superposition caused by full-band analysis. Extracting the maximum peak frequency and its amplitude within each band helps capture the abnormal reflection behavior of metallic foreign objects in response to microwaves within different frequency ranges, enhancing the multidimensional representation of defect features. Furthermore, this segmented processing method simplifies signal processing complexity, improves algorithm efficiency and stability, and provides clearly structured and hierarchically distinct basic data for subsequent defect modeling, thereby significantly improving the accuracy and robustness of metallic residue detection.
[0073] In one embodiment, the step of calculating the amplitude factor corresponding to each microwave reflection characteristic frequency based on each microwave reflection characteristic frequency and its corresponding amplitude includes:
[0074] Calculate the amplitude factor corresponding to each microwave reflection characteristic frequency using the following expression:
[0075]
[0076] in, Indicates the first The amplitude factor corresponding to each microwave reflection characteristic frequency. Indicates the first The amplitude corresponding to each microwave reflection characteristic frequency. Indicates the first One microwave reflection characteristic frequency, Indicates the first Frequency weights of microwave reflection characteristic frequencies.
[0077] In this embodiment, by multiplying the amplitude by a frequency weight, the reflected signal in a specific frequency band can be weighted to enhance or suppress it, thereby strengthening the response characteristics at frequencies that are physically more sensitive or more easily interfered with by metallic foreign objects. This processing method not only retains the intensity information provided by the original amplitude but also introduces the identification value of the frequency dimension, making the calculation results more targeted and discriminative. It also improves the distinguishability and stability of the amplitude factor, effectively reducing over-response to irrelevant frequencies, thus enhancing the robustness and accuracy of subsequent calculations of the metallic residue factor. Furthermore, by reasonably setting the weighting function... It can flexibly adapt to the characteristic response patterns of different cable types and application scenarios, thereby improving the adaptability, scalability and engineering practicality of the entire testing method.
[0078] In one embodiment, the expression for the frequency weight of each microwave reflection characteristic frequency is:
[0079]
[0080] in, Indicates the first Frequency weights of microwave reflection characteristic frequencies Indicates the first One microwave reflection characteristic frequency.
[0081] In this embodiment, by normalizing the microwave reflection characteristic frequency to the sum of all characteristic frequencies, the characteristic frequencies corresponding to the high-frequency components can obtain a higher weight in the amplitude factor. Since high-frequency microwave signals are more sensitive to small metal foreign objects inside the cable and have a stronger reflection response, this weighting expression can effectively enhance the high-frequency band's response to defects and improve the accuracy of detection for early, small metal residues. Simultaneously, the formula is simple, easy to calculate, and facilitates rapid deployment; its normalization characteristic ensures consistency and comparability of calculations under different frequency distribution conditions, avoiding result imbalances caused by differences in frequency values. Furthermore, by dynamically calculating rather than fixing the weights, this method has stronger adaptability, maintaining the stability and universality of detection results under different cable structures or scenarios, thereby significantly improving the engineering practicality and intelligence level of the entire detection system.
[0082] In one embodiment, the step of calculating the metal residual factor of the cable to be evaluated based on the amplitude and amplitude factor corresponding to each microwave reflection characteristic frequency includes:
[0083] The metal residue factor is calculated using the following expression:
[0084]
[0085] in, Indicates metal residue factor, Indicates the first The amplitude factor corresponding to each microwave reflection characteristic frequency. Indicates the first The amplitude corresponding to each microwave reflection characteristic frequency. This indicates the outer radius of the cable insulation layer of the cable to be evaluated. This indicates the conductor radius of the cable to be evaluated. Indicates the nonlinear factor. This represents the relative permittivity of the insulating material.
[0086] In this embodiment, the three amplitude factors are nonlinearly fused in the numerator. Compared to simple linear weighting, this significantly amplifies anomalous feature points with high reflection intensity and high frequency weights, thereby improving the sensitivity to abnormal responses to local metal residues in cables and enhancing detection accuracy. The introduction of nonlinear factors also gives the system adjustability, facilitating dynamic control of response characteristics under different detection requirements. Secondly, the cable geometric parameters and their square terms are introduced into the denominator, which normalizes the structural differences of cables of different sizes, avoiding systematic errors caused by variations in cable thickness. Simultaneously, by combining the relative permittivity of the insulating medium, the differences in electromagnetic response characteristics of different cable materials are further compensated, making the calculation of the metal residue factor more physically realistic and engineering-comparable. Thirdly, the summation of the original amplitudes and their introduction into the denominator allows for adjustment of the overall signal strength level, thereby avoiding misleading defect judgments due to large background responses and improving the relative resolution of the residue factor.
[0087] In summary, this calculation formula, while preserving reflection characteristics, fully incorporates multiple factors such as frequency weighting, structural normalization, and material compensation. It can accurately characterize the degree of disturbance of the microwave response of cables by metallic foreign objects, and has good stability, sensitivity, and adaptability. This significantly improves the reliability and quantification of the detection of metallic residual defects, meeting the detection needs of various types and specifications of cables in practical engineering.
[0088] In one embodiment, the step of assessing the size of residual metal defects in the cable to be evaluated based on a residual metal factor includes:
[0089] If the metal residue factor is greater than zero and not greater than the first preset threshold, then the metal residue defect of the cable to be evaluated is small.
[0090] If the metal residue factor is greater than the first preset threshold and not greater than the second preset threshold, then the metal residue defect of the cable to be evaluated is moderate.
[0091] If the metal residue factor is greater than the second preset threshold but not greater than 1, then the cable to be evaluated has a large metal residue defect.
[0092] Among them, the first preset threshold and the second preset threshold are pre-set boundary values used to determine the degree of metal residue. They are obtained based on a large amount of historical detection data and actual failure cases, and correspond to the boundaries of small and medium defect levels, respectively.
[0093] Specifically, the computer device obtains the calculated metal residue factor from the preceding module and inputs it into the defect level assessment module. This module is pre-configured with two threshold parameters, namely a first preset threshold and a second preset threshold. Their values can be manually set based on engineering experience or automatically generated by the system during the training phase based on labeled samples.
[0094] Subsequently, conditional judgment logic is used to classify the metal residue factor value into intervals. Specifically, when the metal residue factor meets the condition of being greater than 0 and not greater than the first preset threshold, it is judged as a small metal residue defect, indicating that only a weak anomaly occurs in microwave reflection and does not affect the stability of cable operation; when the metal residue factor is between the first and second preset thresholds, the output defect is medium, indicating that the cable has a certain degree of metal foreign object interference, and it is recommended to continue to observe or strengthen inspections; when the metal residue factor exceeds the second preset threshold but does not exceed 1, it is judged as a large defect, indicating that the microwave reflection is abnormally significant, and it is recommended to immediately investigate or replace the cable to prevent potential breakdown or partial discharge risks.
[0095] In one embodiment, the first preset threshold is 0.359807, and the second preset threshold is 0.371104. These two values are used to divide the metal residue factor β into different evaluation intervals to achieve a quantitative judgment on the degree of metal residue defects in the cable. The above thresholds can be determined based on a large amount of historical sample data through statistical analysis or machine learning methods, or they can be set in combination with experimental test results and engineering experience, possessing clear physical meaning and application background. Specifically, when the metal residue factor is not greater than 0.359807, it indicates that the interference of metal residue on microwave reflection is small, and the cable is in a normal or slightly defective state; when the metal residue factor is between 0.359807 and 0.371104, it reflects that the influence of metal interference is gradually increasing, and continuous monitoring is recommended; when the metal residue factor exceeds 0.371104, it can be considered that there is significant metal foreign matter residue in the cable, posing a high risk of failure, which requires attention. This classification method not only improves the sensitivity and interpretability of the detection results but also helps to achieve differentiated management of defect handling, thereby enhancing the practicality and safety assurance capabilities of the detection system.
[0096] It is understandable that segmenting continuous metal residue factor values into three levels—"smaller," "medium," or "larger"—not only improves the interpretability and practical operability of the detection results, enabling technicians to quickly understand the cable condition, but also facilitates automated screening and intelligent grading in large-scale monitoring. Introducing two preset thresholds for multi-level classification helps improve the accuracy and flexibility of risk identification, allowing cable management strategies to be differentiated based on different defect severity levels, thus improving resource allocation efficiency. Furthermore, the logic-based implementation is simple and clear, suitable for embedded deployment or integration with edge computing platforms, thereby enhancing the system's engineering adaptability and deployment efficiency. In summary, this embodiment significantly improves the practicality, intelligence, and safety assurance capabilities of the cable metal residue detection system.
[0097] The following describes the 10kV XLPE cable metal residue size assessment device provided in the embodiments of this application. The 10kV XLPE cable metal residue size assessment device described below can be referred to in correspondence with the 10kV XLPE cable metal residue size assessment method described above. Figure 2 As shown, this application provides a device for evaluating the size of residual metal in a 10kV XLPE cable. The device includes:
[0098] The microwave reflection curve acquisition module 201 is used to radially inject microwave signals with a frequency band of 22GHz~30GHz into the 10kV XLPE cable to be evaluated, and extract multiple microwave reflection characteristic frequencies and their corresponding amplitudes based on the acquired microwave reflection curve.
[0099] The amplitude factor calculation module 202 is used to calculate the amplitude factor corresponding to each microwave reflection characteristic frequency based on each microwave reflection characteristic frequency and its corresponding amplitude.
[0100] The metal residual factor calculation module 203 is used to calculate the metal residual factor of the cable to be evaluated based on the amplitude and amplitude factor corresponding to each microwave reflection characteristic frequency.
[0101] The metal residual defect size assessment module 204 is used to assess the size of the metal residual defects in the cable to be assessed based on the metal residual factor.
[0102] In one embodiment, the microwave reflection curve acquisition module 201 includes:
[0103] The frequency band division unit is used to divide the frequency band of microwave signals into low frequency band, mid frequency band and high frequency band, wherein the low frequency band is 22GHz~24GHz, the mid frequency band is 24GHz~28GHz, and the high frequency band is 28GHz~30GHz;
[0104] The microwave reflection characteristic frequency determination unit is used to extract the frequencies and amplitudes corresponding to the maximum peak values in the low-frequency, mid-frequency, and high-frequency bands of the microwave reflection curve, respectively, to obtain multiple microwave reflection characteristic frequencies and their amplitudes.
[0105] In one embodiment, the amplitude factor calculation module 202 includes:
[0106] The amplitude factor calculation unit is used to calculate the amplitude factor corresponding to each microwave reflection characteristic frequency according to the following expression:
[0107]
[0108] in, Indicates the first The amplitude factor corresponding to each microwave reflection characteristic frequency. Indicates the first The amplitude corresponding to each microwave reflection characteristic frequency. Indicates the first One microwave reflection characteristic frequency, Indicates the first Frequency weights of microwave reflection characteristic frequencies.
[0109] In one embodiment, the expression for the frequency weight of each microwave reflection characteristic frequency is:
[0110]
[0111] in, Indicates the first Frequency weights of microwave reflection characteristic frequencies Indicates the first One microwave reflection characteristic frequency.
[0112] In one embodiment, the metal residue factor calculation module 203 includes:
[0113] The metal residue factor calculation unit is used to calculate the metal residue factor according to the following expression:
[0114]
[0115] in, Indicates metal residue factor, Indicates the first The amplitude factor corresponding to each microwave reflection characteristic frequency. Indicates the first The amplitude corresponding to each microwave reflection characteristic frequency. This indicates the outer radius of the cable insulation layer of the cable to be evaluated. This indicates the conductor radius of the cable to be evaluated. Indicates the nonlinear factor. This represents the relative permittivity of the insulating material.
[0116] In one embodiment, the metal residual defect size assessment module 204 includes:
[0117] The first metal residual defect size assessment unit is used to determine that if the metal residual factor is greater than zero and not greater than the first preset threshold, then the metal residual defect of the cable to be assessed is relatively small.
[0118] The second metal residual defect size assessment unit is used to determine that the metal residual defect of the cable to be assessed is medium if the metal residual factor is greater than the first preset threshold and not greater than the second preset threshold.
[0119] The third metal residual defect size assessment unit is used to determine that if the metal residual factor is greater than the second preset threshold and not greater than 1, then the metal residual defect of the cable to be assessed is relatively large.
[0120] In one embodiment, the first preset threshold is 0.359807 and the second preset threshold is 0.371104.
[0121] In one embodiment, this application also provides a storage medium storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the 10kV XLPE cable metal residue size assessment method as described in any of the above embodiments.
[0122] In one embodiment, this application also provides a computer device storing computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the 10kV XLPE cable metal residue size assessment method as described in any of the above embodiments.
[0123] Indicatively, such as Figure 3 As shown, Figure 3 This is a schematic diagram of the internal structure of a computer device 300 provided in an embodiment of this application. The computer device 300 can be provided as a server. (Refer to...) Figure 3 The computer device 300 includes a processing component 302, which further includes one or more processors, and memory resources represented by memory 301 for storing instructions, such as application programs, that can be executed by the processing component 302. The application programs stored in memory 301 may include one or more modules, each corresponding to a set of instructions. Furthermore, the processing component 302 is configured to execute instructions to perform the 10kV XLPE cable metal residue size assessment method of any of the above embodiments.
[0124] The computer device 300 may also include a power supply component 303 configured to perform power management of the computer device 300, a wired or wireless network interface 304 configured to connect the computer device 300 to a network, and an input / output (I / O) interface 305. The computer device 300 may operate on an operating system stored in memory 301, such as Windows Server™, Mac OS X™, Unix™, Linux™, Free BSD™, or similar.
[0125] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.
[0126] Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. In this document, "a," "an," "the," "the," and "its" may also include plural forms unless the context clearly indicates otherwise. "Multiple" refers to at least two, such as 2, 3, 5, or 8, etc. "And / or" includes any and all combinations of the related listed items.
[0127] The various embodiments in this specification are described in a progressive manner. Each embodiment focuses on the differences from other embodiments. The various embodiments can be combined as needed, and the same or similar parts can be referred to each other.
[0128] The above description of the disclosed embodiments enables those skilled in the art to make or use this application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of this application. Therefore, this application is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A method for evaluating the size of residual metal in a 10kV XLPE cable, characterized in that, The method includes: For the 10kV XLPE cable to be evaluated, a microwave signal with a radial frequency band of 22GHz~30GHz is injected. The frequency band of the microwave signal is divided into low frequency band, mid frequency band and high frequency band, wherein the low frequency band is 22GHz~24GHz, the mid frequency band is 24GHz~28GHz, and the high frequency band is 28GHz~30GHz. From the obtained microwave reflection curve, the frequency and amplitude corresponding to the peak value in the low frequency band, the mid frequency band and the high frequency band are extracted respectively to obtain multiple microwave reflection characteristic frequencies and their corresponding amplitudes. Based on each microwave reflection characteristic frequency and its corresponding amplitude, calculate the amplitude factor corresponding to each microwave reflection characteristic frequency; wherein, the amplitude factor corresponding to each microwave reflection characteristic frequency is calculated according to the following expression: in, Indicates the first The amplitude factor corresponding to each of the microwave reflection characteristic frequencies. Indicates the first The amplitude corresponding to each of the microwave reflection characteristic frequencies. Indicates the first The microwave reflection characteristic frequency is described above. Indicates the first The frequency weights of the microwave reflection characteristic frequencies are expressed as follows: The metal residue factor of the cable to be evaluated is calculated based on the amplitude and amplitude factor corresponding to each microwave reflection characteristic frequency; wherein the metal residue factor is calculated according to the following expression: in, This refers to the metal residue factor. This indicates the outer radius of the cable insulation layer of the cable to be evaluated. This indicates the conductor radius of the cable to be evaluated. Indicates the nonlinear factor. This represents the relative permittivity of an insulating material; The size of the metal residual defect in the cable to be evaluated is determined based on the metal residual factor.
2. The method for evaluating the size of residual metal in a 10kV XLPE cable according to claim 1, characterized in that, The step of evaluating the size of the residual metal defects in the cable to be evaluated based on the residual metal factor includes: If the metal residue factor is greater than zero and not greater than the first preset threshold, then the metal residue defect of the cable to be evaluated is small. If the metal residue factor is greater than the first preset threshold and not greater than the second preset threshold, then the metal residue defect of the cable to be evaluated is moderate. If the metal residue factor is greater than the second preset threshold and not greater than 1, then the metal residue defect of the cable to be evaluated is large.
3. The method for evaluating the size of residual metal in a 10kV XLPE cable according to claim 2, characterized in that, The first preset threshold is 0.359807, and the second preset threshold is 0.371104.
4. A device for assessing the size of residual metal in a 10kV XLPE cable, characterized in that, The device includes: The microwave reflection curve acquisition module is used to radially inject microwave signals with a frequency band of 22GHz~30GHz into a 10kV XLPE cable to be evaluated, and divide the frequency band of the microwave signal into low frequency band, mid frequency band and high frequency band, wherein the low frequency band is 22GHz~24GHz, the mid frequency band is 24GHz~28GHz, and the high frequency band is 28GHz~30GHz. From the acquired microwave reflection curve, the frequency and amplitude corresponding to the maximum value of the peak value in the low frequency band, the mid frequency band and the high frequency band are extracted respectively to obtain multiple microwave reflection characteristic frequencies and their corresponding amplitudes. The amplitude factor calculation module is used to calculate the amplitude factor corresponding to each microwave reflection characteristic frequency based on each microwave reflection characteristic frequency and its corresponding amplitude; wherein, the amplitude factor corresponding to each microwave reflection characteristic frequency is calculated according to the following expression: in, Indicates the first The amplitude factor corresponding to each of the microwave reflection characteristic frequencies. Indicates the first The amplitude corresponding to each of the microwave reflection characteristic frequencies. Indicates the first The microwave reflection characteristic frequency is described above. Indicates the first The frequency weights of the microwave reflection characteristic frequencies are expressed as follows: The metal residue factor calculation module is used to calculate the metal residue factor of the cable to be evaluated based on the amplitude and amplitude factor corresponding to each of the microwave reflection characteristic frequencies; wherein, the metal residue factor is calculated according to the following expression: in, This refers to the metal residue factor. This indicates the outer radius of the cable insulation layer of the cable to be evaluated. This indicates the conductor radius of the cable to be evaluated. Indicates the nonlinear factor. This represents the relative permittivity of an insulating material; The metal residual defect size assessment module is used to assess the size of the metal residual defects in the cable to be assessed based on the metal residual factor.
5. A storage medium, characterized in that: The storage medium stores computer-readable instructions that, when executed by one or more processors, cause the one or more processors to perform the steps of the method for evaluating the size of residual metal in a 10kV XLPE cable as described in any one of claims 1 to 3.
6. A computer device, characterized in that, include: One or more processors, and memory; The memory stores computer-readable instructions, which, when executed by the one or more processors, perform the steps of the method for evaluating the size of residual metal in a 10kV XLPE cable as described in any one of claims 1 to 3.